Pile side friction is an important component of the bearing capacity of socketed piles, and plays a crucial role in improving the vertical bearing capacity of socketed piles. In order to study the load-bearing mechanism of socketed piles, on the study background of on-site monitoring of the bearing capacity of socketed piles of Qingchi Grand Bridge on Guiyang-Jinsha-Gulin Expressway, the development mechanism of pile side friction under construction loads is analyzed. Based on the shear characteristics of the pile-rock interface of socketed pile and theoretical analysis, the expressions of pile side friction and shear displacement are derived. A load transfer model of socketed pile is established based on the load transfer theory, and the model is solved by using the 4th-order Runge-Kutta method through Python. The on-site monitoring data of engineering piles are in accordance with the calculation result, indicating the correctness of the established load transfer model and the rationality of the calculation method, which can be used for study the mechanism of side friction of socketed piles. According to the monitoring data and the load transfer model, the characteristics of the axial force and shear displacement of the pile foundation section in the shear slip stage under different construction loads are analyzed. The result shows that (1) Under construction loads, the pile side friction of socketed pile plays a role from top to bottom gradually and bears most of the construction loads. (2) When the shear displacement reaches the ultimate shear displacement, the pile side friction remains stable gradually, and the construction loads are borne by the pile end resistance gradually. (3) The sectional axial force of socketed pile calculated by using the load transfer model is in accordance with the monitoring result, upon that the established load transfer model and calculation method have practical engineering significance. (4) The calculated ultimate pile end resistance is significantly smaller than that calculated according to the Design Specification for Highway Bridges and Culverts Foundations (JTG 3363—2019). This indicates that the original design of the ultimate pile end resistance is overly conservative and needs to be reasonably revised.

Based on data preprocessing, digital analysis of the asphalt pavement construction process was conducted, and important construction processes and meteorological parameters affecting construction temperature were screened using the random forest (RF) algorithm. Based on the selected important parameters, the construction temperature prediction model was established by multi-layer perception (MLP). Based on feedback control theory, the control principle of the PID controller was analyzed, and a comprehensive and multi-stage temperature feedback control model was constructed in conjunction with the construction temperature prediction model. In order to solve the problem that the super-parameter cannot be self-tuning, the genetic algorithm (GA) is used to optimize the feedback control model to make the model adaptive. The feedback control model's construction process decision was compared and analyzed with the actual construction process parameters, and the robustness of the model's feedback control results was evaluated to effectively adjust the construction process parameters and achieve precise control of asphalt mixture construction temperature. The research results showed that the construction temperature prediction model could accurately predict the construction temperature. The comprehensive feedback control model could feedback control all parameters, while the multi-stage feedback control model could maintain the determined parameters and feedback control other parameters. In addition, the GA-PID feedback control model based on genetic algorithms had the performance of adaptive tuning of hyperparameters. Through analysis of the feedback control results, it was found that the construction process parameters obtained from the GA-PID feedback control model were evenly distributed within the effective range of actual process parameters, maintaining good consistency with the actual construction process parameters. The GA-PID control system had good robustness for different prediction models and different temperature control, and the proposed construction process decision was consistent with actual situations.